China Safety Science Journal ›› 2020, Vol. 30 ›› Issue (5): 39-47.doi: 10.16265/j.cnki.issn1003-3033.2020.05.007

• Safety engineering technology • Previous Articles     Next Articles

Prediction of gas emission quantity based on KPCA-CMGANN algorithm

XIAO Peng1,2, XIE Xingjun1,2, SHUANG Haiqing1,2, LIU Chaoyang3, WANG Haining3, XU Jingcang3   

  1. 1. College of Safety Science and Engineering, Xi'an University of Science & Technology, Xi'an Shaanxi 710054, China;
    2. Key Laboratory of Western Mine Exploitation and Hazard Prevention, Ministry of Education, Xi'an University of Science & Technology, Xi'an Shaanxi 710054, China;
    3. Shaanxi Chenghe Mining Co., Ltd., Chengcheng Shaanxi 715200, China
  • Received:2020-02-05 Revised:2020-04-15 Online:2020-05-28 Published:2021-01-28

Abstract: In order to accurately predict gas emission quantity, considering the nonlinearity, time-varying characteristic and complexity of absolute gas emission, KPCA was proposed to conduct dimensionality reduction for influencing factors. Secondly, targeting at problems of BPNNs' slow convergence and tendency to fall into local optimal solution, CMGA was adopted to optimize BPNN. Then, a coupling algorithm CMGANN based on CMGA and BPNN was constructed to calculate and analyze sample sets formed by historical data of a low gas mine, and KPCA-CMGANN prediction model was established which together with three other network models were used to predict coal mine field data. The results show that KPCA-CMGANN model achieves convergence in 379 time steps, and relative errors of gas emission prediction for four working faces are 0.58%, 0.63%, 0.57% and 0.45% with an average relative error at only 0.56%. Its prediction accuracy and convergence speed are superior to comparative model, making it ready to predict gas emission amount accurately and quickly.

Key words: predication of gas emission quantity, kernel principal component analysis (KPCA), compression mapping genetic algorithm (CMGA), back propagation neural network(BPNN), sample sets

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